H43014046

C. S. Sumathi et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 3( Version 1), March 2014, pp.40-46
www.ijera.com 40 | P a g e
Enhancing Accuracy of Plant Leaf Classification Techniques
C. S. Sumathi*, A. V. Senthil Kumar **
*(Assistant Professor, School of Post Graduate Studies, Tamil Nadu Agricultural University, Coimbatore-3,
Tamil Nadu, India.)
** (Director, Department of Post Graduate and Research in Computer Applications, Hindusthan College of Arts
and Science, Coimbatore – 641028, Tamil Nadu, India)
ABSTRACT
Plants have become an important source of energy, and are a fundamental piece in the puzzle to solve the
problem of global warming. Living beings also depend on plants for their food, hence it is of great importance to
know about the plants growing around us and to preserve them. Automatic plant leaf classification is widely
researched. This paper investigates the efficiency of learning algorithms of MLP for plant leaf classification.
Incremental back propagation, Levenberg–Marquardt and batch propagation learning algorithms are
investigated. Plant leaf images are examined using three different Multi-Layer Perceptron (MLP) modelling
techniques. Back propagation done in batch manner increases the accuracy of plant leaf classification. Results
reveal that batch training is faster and more accurate than MLP with incremental training and Levenberg–
Marquardt based learning for plant leaf classification. Various levels of semi-batch training used on 9 species of
15 sample each, a total of 135 instances show a roughly linear increase in classification accuracy.
Keywords: Back Propagation, Incremental back propagation, Levenberg–Marquardt Algorithm, Multi-Layer
Perceptron (MLP).
I. Introduction
Plant features like fruit, seed, leaf, flower,
root, stem etc., help in identifying a plant. As the
shape of plant leaves in one of the most important
features for characterising various plants visually, the
study of leaf image retrieval schemes will be an
important stage for developing a plant identification
system [10]. The existing electronic herbarium
identifies species based on the taxonomic inputs from
the user , but it is essential to have a mechanized leaf
recognition system, for easy access of the public.
There are many ways to plant identification. The
conventional methods commonly used are expert
determination, recognition, comparison and use of
keys and similar devices. These methods are
advantageous in their own way [1].
The expert determination is the best option
in terms of reliability or accuracy for leaf
classification. However, the identification process
may consume considerable amount of time even for
the experts. Recognition is also considered reliable,
next to expert determination. But there are cases
where the method becomes inapplicable. Comparison
is also consistent but extremely time-consuming.
Given a large data set, comparing two plants at a time
would be virtually impossible. The option is claimed
to be the most widely used method since it does not
require much time, materials or experience unlike
other methods[1].
An Artificial Neural Network (ANN) is an
information processing paradigm that is inspired by
the way biological nervous systems, such as the
brain, process information. ANN is the computational
model formed from several of single units, artificial
neurons, connected with coefficients (weights) which
constitute the neural structure. The key element of
this paradigm is the Processing Elements (PE) as they
process information. Each PE has weighted inputs,
transfer function and one output. PE is essentially an
equation which balances inputs and outputs. It is
composed of large number of highly interconnected
processing neurons working in union to solve specific
problems. ANNs learn by example like human. An
ANN is configured for a specific application, such as
pattern recognition or data classification, through a
learning process. Learning in biological systems
involves adjustments to the synaptic connections that
exist between the neurones. ANN is a system loosely
modelled on the human brain [2].
Many types of neural networks are designed,
all can be described by the transfer functions of their
neurons, by the training or learning algorithm (rule),
and by the connection formula. A single-layer neuron
is not able to learn and generalize the complex
problems. The MLP overcomes the limitation of the
single-layer perceptron by the addition of one or
more hidden layer(s).
The MLP has been proven to be a universal
approximator [3], a feed forward multilayer
perceptron network was presented. MLPs are feed-
forward neural networks organised in layers. The
input layer consists of distribution points, one or
RESEARCH ARTICLE OPEN ACCESS

more hidden layers of artificial neurons and one
output layer of artificial neurons (nodes). Each node
in a layer is connected to all other nodes in the next
layer and has a weight. MLPs are often trained using
error back propagation. The activation (transfer)
function acts on the weighted sum of the neuron‘s
inputs and the most commonly used transfer function
is the sigmoid (logistic) function.
II. Related works
Gradient-based optimization algorithms are
the standard methods for adapting the weights of
neural networks [4]. The natural gradient gives the
steepest descent direction based on a non-Euclidean,
from a theoretical point of view more appropriate
metric in the weight space. They empirically
compared Rprop(resilient back propagations) using
the Euclidean and non-Euclidean metric respectively.
As their work is closely related to Levenberg-
Marquardt learning, this method is added for
comparison. Rprop based on the non-Euclidean
metric shows at least similar performance as
Levenberg-Marquardt learning on the two benchmark
problems considered and appears slightly more
robust. In both benchmark problems the task of
learning the sample data is considered and not the
import issue of generalization. It turned out that the
Rprop algorithm can indeed profit from using the
natural gradient, although the updates done by Rprop
are not collinear with the gradient direction. Natural
iRprop+
shows similar performance as Levenberg-
Marquardt learning on two test problems. The results
indicate the natural iRprop+
is little bit faster in the
early stages of optimization. Levenberg-Marquardt
learning and Rprop using the natural gradient
computing a weight update requires cubic time and
quadratic space.
A new incremental learning method for
pattern recognition called ‗Incremental back
propagation learning network‘ was proposed by
Limin [5]. An incremental learning system learns y
based on x, then learns z based on y and son on. The
standard back propagation network is not an
incremental learning by its nature. Two problem
domain were used to evaluate the learning system.
There were 150 instances of three classes viz., setose,
verlicolor and viginica, belonging to Iris flowers.
Four attributes such as septal length, septal width,
petal length and petal width were considered for each
instance. Each attribute of all instances was
discriticized into three levels. The second domain
was the recognition of promoters in DNA nucleotide
strings. The performance of an incremental learning
system was evaluated in respect of memorization of
old knowledge and generalization to unseen
instances. The learning curves in all these cases show
smooth convergence with minor fluctuation through
the process. Thus a new incremental learning method
for pattern recognition IBPLN, which employs
bounded weight modification and structural adaption
learning rules and applies initial knowledge to
constraint the learning rule has been proposed in this
work.
The analysis of two training algorithm
Bayesian Regularization and Levenberg-Marquardt
based on MLP neural network reveals that MLP can
solve difficult and diverse problem in supervised
manner with error back-propagation algorithm [6]. In
back propagation algorithm error is to back propagate
to adjust the weights to reduce the error between the
actual output and estimated output. In their analysis
they have simulated the MLP network and computed
the localization error. Artificial Neural Network with
3 dimensional inputs have been used and one hidden
layer with 15 neurons and two outputs. 121 data
sample have been created for training the network.
The Bayesian regularisation algorithm is more
accurate as compared to Levenberg-Marquardt
algorithm. The algorithm also reduces the need for
lengthy cross-validation. It has an efficient criterion
for stopping the training process and prevents
overtraining of the network. This ability makes it a
more adaptive and robust back-propagation network
for evolving localization algorithm for wireless
sensor network. The simulation results demonstrate
the effectiveness of the proposed model on
localization error.
The effect of two causal factors viz., coating
weight gain and amount of pectin-chitosan in the
coating solution of the vitro release profile of
theophylline for biomodal drug delivery was
modelled by incorporating ANN multilayer
perceptron feed forward network and developed a
predictive model of formulation [7]. Five different
training algorithms of three classes, gradient descent,
Levenberg-Marquardt and Genetic Algorithm (GA)
were used to train NN containing a single hidden
layer of four nodes. Subsequently, the performance of
aforementioned algorithm was compared with regard
to predicting ability. The ANNs were trained with
those algorithms using the existing experimental data
as the training set. Though GA is often useful in a
robust evaluation of the best region of a solution
space; it is inefficient and ineffective in fine-tuning
local search with their problem‘s region. Further,
training by GA often requires relatively long
computational time. Incremental backpropagation
and batch backpropagation outperformed the others.
Gradient descent backpropagation algorithm in
particular incremental and batch propagation can be
used as the training algorithms for modelling and
prediction of in vitro drug release profiles.
The concept of supervised learning in multi-
layer perceptrons based on the technique of gradient

descent was introduced by Wilson and Martin [8].
Few problems and drawbacks of the original
backpropagation learning procedure are discussed
and more sophisticated technique is developed. The
performance of several algorithms is tested in twenty
runs with different initial weight setting. The fast and
robust convergence of adaptive learning algorithms,
and the failure of pure gradient, demonstrates the
ability of advanced techniques to solve very complex
learning tasks. Thus this article gives an overview
over past and recent developments in algorithms for
supervised learning in multi-layer perceptron.
The use of entropy as a cost function in the
neural network learning phase usually implies that, in
the back-propagation algorithm, the training is done
in batch mode [9]. They present a way of combining
both modes when using entrophic criteria, taking
profit of the advantages of both methods. The batch-
sequential algorithm tries to combine the two
methods applied in the back propagation learning
algorithm, the sequential mode and the batch mode
where the update is performed after the presentation
of all samples of the training set. The experiments
show that this is a valid approach that can be used to
speed-up the training phases, maintaining a good
performance.
An application was developed using Cany
Edge Detection and multi-layer perceptron for
recognizing leaves of topical plants [10]. It
recognizes a plant from an input image file using the
plant leaf‘s shape. Hybrid modelling techniques is
used to extract features from the leaf. The moment-
invariant method is used to extract the first four
moments of the image while the centroid-radii
method is used to extract 36 radii with respect to the
images centroid. Canny Edge Detection technique is
used in extracting the edges of the leaf images, which
undergo pattern recognition process using multi-layer
perceptron. It lists the possible matches for the plant
species depending on the training set. This
application would be helpful to the researchers and
botanist.
Artificial neural network is used to identify
plant by inputting leaf image is described by Hati
[11]. A new input features and image processing
approach that matters in efficient classification in
artificial neural network have been introduces
compared to earlier approaches. Image processing
techniques are used to extract leaf shape features
such as aspect ratio, width ratio, apex angle, apex
ratio, base angle, centroid deviation ratio, apex ratio
and circularity. These extracted features are given as
input to neural network. 534 leaves of 20 species of
plants were collected, out of which 400 leaves were
trained and 234 testing samples were recognized with
92% accuracy.
III.Material and methods
3.1 Feature Extraction
Edge detection is the process of detecting
the pixels in the image that represent the edges of the
image object. This edge detection process consists of
three steps such as: filtering, enhancement and
detection. Noise in the image removed during
filtering due to random variation in intensity value.
Further improvement intensifies the pixels while
there is a change in local intensity. Edges are
detected using thresholding concept. Prewitt edge
detection, Robert edge detection, Sobel edge
detection and canny edge detection are most
commonly used detection methods and Sobel edge
detector is proposed here.
The Sobel edge detector finds the
approximate absolute gradient magnitude to detect
edges at each point. Regions of high spatial
frequency corresponding to edge are obtained by the
2-D gradient measurement. A series of gradient
magnitudes can be created using a simple
convolution kernel and this convolution can be
mathematically represented as,
     
1 1
1 1
, , ,
k j
N x y K j k p x j y k
 
   
(1)
The Sobel detector uses two convolution
kernels for detecting changes in horizontal contrast
(hy) and vertical contrast (hx).
Gabor filters are bandpass filters which are
used in image processing for feature extraction,
texture analysis. Its impulse response is defined by a
harmonic function multiplied by a Gaussian function.
Thus, a bidimensional Gabor filter constitutes a
complex sinusoidal plane of particular frequency and
orientation modulated by a Gaussian envelope [12]. It
achieves an optimal resolution in both spatial and
frequency domains. The impulse response of these
filters is created by multiplying a Gaussian envelope
function with a complex oscillation.
Let [ 1 2]x x x T be the image coordinates.
The impulse response of a Gabor filter g(x) is then
given by,
0
1
2
1
( )
2
T x
T xmn
mn
n n
x A
jK
mng x e e
a b

 (2)
A Gabor function is a sinusoidal modulated Gaussian
in the spatial domain. For a 2-D Gaussian curve with
a spread of
andx y 
in the x and y directions,
respectively, and a modulating frequency of 0u , the
real impulse response of the filter [15] is given by,
   
2 2
02 2
1 1
,| exp .cos 2
2 2x y x y
x y
h x y u x
   
   
    
     (3)

3.2 Feature Selection
Feature selection is the process of deciding
on a subset of relevant features for use in system
construction. The feature selection technique is
deployed because the data contains
many redundant or irrelevant features in it.
Redundant features are those which provide only
information about the currently selected features and
irrelevant features provide no constructive
information. It is important to indulge feature
selection as it improves the performance of
classification algorithm and allows understanding the
domain better. Given a set of features V and a target
variable T, the minimum set F that achieves
maximum classification performance of T gives the
best feature.
Correlation based Feature Selection (CFS) is
a filter algorithm which ranks feature subsets based
on a correlation based heuristic evaluation function.
The bias of the evaluation function is toward subset
which includes a feature that is highly correlated with
the class and uncorrelated with each other. Empirical
evidence from the feature selection literature shows
that, along with irrelevant features, redundant
information.CFS algorithm that couples feature
evaluation formula with an appropriate correlation
measure and a heuristic search strategy[13].
A feature iV is said to be relevant iff there
exists some iv and c for which
( ) 0i ip V v  such that
( | ) ( )i ip C c V v p C c   
3.3 MLP with various learning algorithms
Figure 1. Schematic representation of a multilayer
perceptron feed forward network consisting of two
inputs, one hidden layer with four neurons and 14
outputs
Fig. 1 shows MLP feed forward network.
The network consists of two inputs, one hidden layer
with four neurons and 14 outputs.In MLP, various
learning algorithms are available such as Back
Propagation Learning, Quasi-Newton method [14].
ANN learning paradigms can be classified into
supervised, unsupervised and reinforcement learning.
Supervised learning model is the availability of a
supervisor who classifies the training examples into
classes and utilizes the information on the class
membership of each training instance. Some methods
are Error correction learning rule and memory based
learning rule. Unsupervised learning model identifies
the pattern class information. Q-Learning is the
example of those methods. Reinforcement learning
learns through trial and error interactions with its
environment (reward/penalty assignment).
Competitive learning rule and Hebbian learning rule
is the example of this type of learning [15].
3.3.1 MLP with Levenberg–Marquardt based
learning
Levenberg-Marquardt Algorithm (LMA) is
used to solve non-linear least squared problems.
LMA provides a numerical solution to the problem of
minimizing a function. Back propagation algorithm
utilizes the Levenberg-Marquardt algorithm (trainlm)
[16] for training of the network. The trainlm‗is a
network training function that updates weight and
bias values according to Levenberg-Marquardt
optimization. The Levenberg-Marquardt consists
basically in solving (H + λI) δ = g with different λ
values until the sum of squared error decreases. So,
each learning iteration (epoch) will consist of the
following basic steps:
1. Compute the Jacobian (by using finite
differences or the chain rule)
2. Compute the error gradient g = Jt
E
3. Approximate the Hessian using the cross product
Jacobian H = Jt
J
4. Solve (H + λI)δ = g to find δ
5. Update the network weights w using δ
6. Recalculate the sum of squared errors using the
updated weights
7. If the sum of squared errors has not decreased,
discard the new weights, increase λ using v and
go to step 4.
8. Else decrease λ using v and stop.
Variations of the algorithm may include
different values for v, one for decreasing λ and other
for increasing it.
Some advantages of LMA are
1. The learning capability of the LMA is reported
to be superior and
2. LMA has rapid convergence advantages [17].
3. The LMA is suitable for medium size datasets
and fastest among the other training algorithms.
3.3.2 MLP with Batch Backpropagation algorithm
based learning
In batch back propagation learning, the
accumulated weights change, points in the direction
of the true error gradient. This means that a
(4)
(

sufficiently small step in that direction will reduce
the error on the training set as a whole, unless a local
minimum has already been reached. In contrast, each
weight change made during continuous training will
reduce the error for that particular instance, but can
decrease or increase the error on the training set as a
whole. Hence, batch training produces better
accuracy.
Batch learning proceeds as follows:
1. Initialize all weights to small random values.
2. Repeat
3. For each training example do
4. Forward propagate the input features of the
example to determine the MLP'soutputs.
5. Back propagate the error to generate ∆wij for all
weights wij.
6. End for
7. Update the weights based on the accumulated
values ∆wij.
8. Until stopping criteria reached.
3.3.3 MLP with Incremental Backpropagation
algorithm based learning
In incremental approach, the weights are
changed immediately after a training pattern. The
mode of backpropagation algorithm, after each
training example is presented to the system and
propagated through it, and error is calculated and all
connections are modified in backward manner.
Incremental learning proceeds as follows:
Initialize the weights.
Repeat the following steps.
Process one training case.
Update the weights.
IV.Experimental results
Nine species of plant leaves were selected
[10] with 15 samples for each plant species. Sample
image of the plant leaves used is shown in Fig. 2.
Figure 2. Leaf samples used in this work.
Matlab was used to extract the features. The
features extracted were used to train the classification
algorithms. The features were classified using
Incremental Backpropagation, Batch
Backpropagation and Levenberg–Marquardt
algorithms of Multilayer perceptron. Results reveal
that batch training is faster and more accurate than
MLP with incremental training and Levenberg–
Marquardt based learning for plant leaf classification.
Finally, the two most well known coefficients to
determine the retrieval efficiency, precision and
recall, is calculated as follows:
Recall =
Precision =
f Measure = 2
The classification accuracy obtained is given
in Table 1 and Figure 3. Figure 4 and Table 2
tabulates the precision, recall and f Measure for
various algorithms and compared.
Table 1. Classification Accuracy
Technique Used
Classification
accuracy
MLP with Levenberg-
Marquardt based learning 90.37%
MLP with incremental
Backpropagation 91.85%
MLP with Batch
Backpropagation 93.33%
Figure 3. Classification Accuracy
From Table 1 and Fig. 3 it is observed that
the classification accuracy is compared for different
techniques used with MLP. The classification
accuracy of Batch Backpropagation increases by
1.61% than incremental Backpropagation and
increases by 3.28% than Levenberg-Marquardt based
learning method.

Table 2. Precision, Recall and F Measure
Technique Used Precision Recall
f
Measure
MLP with
Levenberg-
Marquardt based
learning 0.9050 0.9037 0.9028
MLP with
incremental
Backpropagation 0.9225 0.9185 0.9180
MLP with Batch
Backpropagation 0.9356 0.9333 0.9335
From Table 2 and Fig. 4 it is observed that
the precision, recall and Fmeasure values are
compared for different techniques used with MLP.
The precision of Batch Backpropagation increases by
1.42% than incremental Backpropagation and
learning method. The recall of Batch
Backpropagation increases by 1.61% than
incremental Backpropagation and increases by 3.28%
than Levenberg-Marquardt based learning method.
The Fmeasure of Batch Backpropagation increases
by 1.69% than incremental Back propagation and
learning method.
Figure 4. Precision, Recall and FMeasure
V. Conclusion
In this study, MLP batch propagation was
proposed. MLP with incremental back propagation
and Levenberg–Marquardt based learning were used
in existing method and was compared with the
proposed method for estimating the accuracy values.
Feature extraction and selection was performed. Nine
species of plant leaves were selected with 15 samples
for each plant species. From experimental results it is
observed that the classification accuracy, precision,
recall and Fmeasures were compared for different
techniques used with MLP. The classification
accuracy of Batch Back propagation increases by
1.61% than incremental Back propagation and
learning method.
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